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        <td><h2><font color="#FFFFFF">Using Templates</font></h2></td>
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<p><br>
    In using Tetrad you will put together a sequence of boxes connected by flowchart arrows. (See <a
            href="how_to_build_a_session.html">How to Build a Session</a>.) Some sequences are so commonly used, that
    Tetrad will insert the entire sequence for you--boxes and arrows--in the workbench all at once.</p>
<p>Templates are added to the active session using the Templates menu in the main workspace. The Templates menu looks
    like this:</p>
<p><img height="237" src="../images/tetrad_menu4.gif" width="326"></p>
<p>An image of each template along with a short description of it follows.</p>
<h3>Search from Loaded Data</h3>
<p><img height="64" src="../images/templates_searchfromdata.gif" width="194"></p>
<p>This template can be used if you simply want to load in a data set and do a search on it. The data set can be either
    continuous or discrete; the options for search algorithms will depend on which type of data set you load. </p>
<h3>Estimate from Loaded Data (Bayes)</h3>
<p><img height="169" src="../images/templates_estimatedatabayes.gif" width="201"></p>
<p>This template is useful if you want to estimate a Bayes instantiatec model (Bayes IM) from a given data set. A Bayes
    estimation requires a data set and a Bayes parmaeterized model (Bayes PM) as input. There are two difficulties in
    getting such an estimation to work:</p>
<ol>
    <li>All of the measured variables in the Bayes PM must occur in the data set. The maximum likelihood (ML) Bayes
        estimator and the Dirichlet estimator both require that all of the variables in the Bayes PM be measured,
        although the Structural EM search allows for latents variables.
    </li>
    <li>For each variable V in the Bayes PM with categories Ci, i = 1,...,ci for some ci &gt; 0, the variable by the
        same name in the data set must have the same categories.
    </li>
</ol>
<p>These conditions can be difficult to ensure when building a Bayes PM from scratch. Adding the edge from Data1 to
    Graph1 in the template creates an edgeless graph in Graph1 that can then be used to construct a specific DAG to use
    to build a Bayes PM. Adding the edge from Data1 to PM1 ensures that the categories for each relevant variable in the
    data set are used when building the Bayes PM. The two arrows out of Data together make it easier to ensure that the
    Bayes estimation will work. </p>
<h3>Estimate from Loaded Data (SEM)</h3>
<p><img height="169" src="../images/templates_estimatedatasem.gif" width="198"></p>
<p>Like the Bayes version of Estimate from Loaded Data, in order to estimate a SEM IM, a continuous data set and a SEM
    PM are required that have the same variables. In this case, however, the variables are always continuous, and
    continuous variables always have the same range (the real numbers), so there is no need to add the edge from Data1
    to PM1. </p>
<h3>Simulate Data</h3>
<p><img height="370" src="../images/templates_simulatedata.gif" width="74"></p>
<p>This is a very useful template for simulating continuous or discrete data sets. Continuous data sets can be simulated
    by constructing a SEM Graph (or DAG), using that to construct a SEM PM, then a SEM IM, and then finally a data set.
    Discrete data sets can be simulated by constructing a DAG, using that to construct a Bayes PM, then a Bayes IM, and
    finally a data set. For information on any one of these steps, see the help files for the corresponding box or
    module.</p>
<h3>Search from Simulated Data</h3>
<p><img height="370" src="../images/templates_searchsimulated.gif" width="239"></p>
<p>This template can be used to try out search algorithms on simulated data. Data can be simulated as with the Simulate
    Data template, and then an appropriate search procedure can be run on this data. Search procedures options are
    different depending on the type of data simulated. </p>
<h3>Search from Simulated Data with Edge Comparisons</h3>
<p><img height="371" src="../images/templates_searchsimcomp.gif" width="240"></p>
<p>This template adds to the Search from Simulated Data a Compare node, which counts the number of extra edges and
    missing edges in the Search graph vis a vis the reference graph in Graph1. This is useful if you want ot get a sense
    of how well a given search procedure performs on data with particular characteristics. </p>
<h3>Estimate from Simulated Data</h3>
<p><img height="369" src="../images/templates_estimatesimulated.gif" width="238"></p>
<p>This template can be used to estimate data with respect to the parametric model that generated it. It is useful if
    you would like to see how well an estimator does on data with particular characteristics, simulated from an
    instantiated model with particular characteristics, when you know the parametric model used to generate it. </p>
<h3>Estimate using Results of Search (Bayes) </h3>
<p><img height="179" src="../images/templates_estsearchresultbayes.gif" width="379"></p>
<p>This template shows how to hook up boxes to estimate data using a model that was generated by a search algorithm on
    that same data. Usually, the graph coming out of Search1 is an equivalence class graph such as a CPDAG or a PAG,
    and some work might be required to turn this into a DAG or SEM Graph in Graph1 that can be used to build an
    appropriate parametric model in PM1. The edge from Data1 to PM1 is added in the discrete case to ensure that the
    variables in PM1 use the same categories as the variables in Data1.</p>
<h3>Estimate using Results of Search (SEM) </h3>
<p><img height="174" src="../images/templates_estsearchresultsem.gif" width="384"></p>
<p>This template shows how to hook up boxes to estimate data using a model that was generated by a search algorithm on
    that same data. Usually, the graph coming out of Search1 is an equivalence class graph such as a CPDAG or a PAG,
    and some work might be required to turn this into a DAG or SEM Graph in Graph1 that can be used to build an
    appropriate parametric model in PM1.</p>
<h3><br>
    Update Bayes IM </h3>
<p><img height="269" src="../images/templates_updatebayesim.gif" width="241"></p>
<p>This template can be used to do updating operations on a Bayes instantiated model that you've built in IM1. </p>
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